Install MLIA and discover capabilities

Check your environment

Use Ubuntu 22.04 LTS or another compatible Linux environment with Python 3.10 or later.

Check that Git Large File Storage (LFS) is installed:

    

        
        
git lfs version

    

If the command fails, install Git LFS and the Python development package:

    

        
        
sudo apt update
sudo apt install -y git-lfs python3.10-dev

    

Create a Python environment

Create a virtual environment so that the Arm ML Inference Advisor (MLIA) packages don’t conflict with any existing ML framework environment:

    

        
        
python3 -m venv mlia_env
source mlia_env/bin/activate
python -m pip install --upgrade pip

    

Install MLIA

MLIA uses plugins. The example target in the Learning Path is Ethos-U, so install the Ethos-U plugin package:

    

        
        
pip install mlia-ethos-u

    

The Ethos-U plugin package depends on a compatible MLIA core package. Installing the target plugin is the recommended starting point because it brings in the matching MLIA core dependency.

Confirm the CLI works

Display top-level help:

    

        
        
mlia --help

    

The output is similar to:

    

        
          check     Generate compatibility/performance advice for a model
  backend   Manage MLIA backends
  target    Manage MLIA targets

        
    

The mlia check command is the main command you’ll use to ask MLIA compatibility and performance questions about model artifacts.

Discover target profiles

MLIA target profiles describe the target configuration used for analysis. List the target profiles available in your environment:

    

        
        
mlia target list

    

For Ethos-U, typical bundled profiles include:

ethos-u85-256 is the Ethos-U85 profile that’s used in the examples. If you want MLIA to evaluate the same model for a different Ethos-U configuration, use a different profile.

Discover backends

Backends perform the work behind an MLIA analysis flow. List available and installed backends:

    

        
        
mlia backend list

    

For this Ethos-U demonstration, expect Vela and Corstone backend options:

    

        
        Name          Installed  Installable
corstone-300  no         yes
corstone-310  no         yes
corstone-320  no         yes
vela          no         yes

        
    

Use Vela for LiteRT and Tensor Operator Set Architecture (TOSA) checks. Use Corstone for packaged ExecuTorch .pte checks.

When you later use mlia check, any missing backends required by your target will be installed.

Clone model artifacts

Clone prebuilt artifacts from the Arm ML model artifacts repository:

    

        
        
git lfs install
git clone --filter=blob:none --sparse https://github.com/arm-education/ml-model-artifacts.git
cd ml-model-artifacts
git sparse-checkout set pte tflite tosa
git lfs pull \
  --include="pte/toy_conditional_select_int8_ethos_u55_256.pte,pte/toy_conditional_select_int8_ethos_u85_256.pte,tflite/mv2_fp32.tflite,tflite/mv2_int8.tflite,tosa/mv2_fp32.tosa,tosa/mv2_int8.tosa" \
  --exclude=""
git lfs checkout

    

This downloads only the artifacts that are required for you to complete the Learning Path. It avoids larger unrelated files, such as transformer .pte, .etdp, and .etrecord artifacts.

Confirm that the artifacts are real model files rather than Git LFS pointer files:

    

        
        
wc -c tflite/mv2_int8.tflite

    

The output is a size of several megabytes, similar to:

    

        
        3942808 tflite/mv2_int8.tflite

        
    

If the file is about 100 to 200 bytes, it’s still a Git LFS pointer file. Run the git lfs pull command again from the ml-model-artifacts directory, then rerun the size check.

The model artifacts are provided for learning and analysis exercises. Use the artifacts to explore MLIA workflows, model formats, and target-aware advice rather than accuracy reference models.

The repository contains model artifacts such as:

    

        
        ml-model-artifacts/
├── pte/
│   ├── toy_conditional_select_int8_ethos_u55_256.pte
│   └── toy_conditional_select_int8_ethos_u85_256.pte
├── tflite/
│   ├── mv2_fp32.tflite
│   └── mv2_int8.tflite
└── tosa/
    ├── mv2_fp32.tosa
    └── mv2_int8.tosa

        
    

What you’ve accomplished and what’s next

You’ve installed MLIA, along with the Ethos-U plugin. You’ve also discovered available target profiles and backends from the CLI, and cloned model artifacts for analysis.

Next, you’ll run your first MLIA compatibility and performance checks.